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Intelligent Decision Making and Bionic Movement Control of Self-Organized Swarm

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第7期68卷 6369-6378页

作者： Yu, Dengxiu Chen, C. L. Philip Xu, Hao Northwestern Polytech Univ Unmanned Syst Res Inst Xian 710072 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China Univ Macau Fac Sci & Technol Macau 99999 Peoples R China North Western Polytech Univ Sch Mech Engn Xian 710072 Peoples R China

In this article, we propose the intelligent decision making and bionic movement control of self-organized swarm. Swarm intelligent decision making can provide swarm path planning. Bionic movement control of self-organized swarm can simultaneously generate swarm formation, realize formation switching, and drive swarm while avoiding collision and preserving the existing communication topology. When the swarm topology switches, the swarm intelligent decision making is introduced to find out the optimal route between the initial position and destination for each intelligent unit. We propose an enhanced genetic algorithm (GA) to realize swarm intelligent decision making. Avoiding collision and preserving the existing communication topology should be achieved during the process of swarm moving. As a result, we design a new swarm motion potential function, which can realize bionic movement control. The stability of bionic movement control is proved to be effective in the collision avoidance, connectivity preservation, and formation generation. Simulation results and experiment results show that the enhanced GA and swarm motion potential function are effective.

关键词： Decision making Topology Collision avoidance Switches genetic algorithms Planning Bionic movement enhanced genetic algorithm intelligent decision making potential function self-organized swarm

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Simultaneous WiFi Ranging Compensation and Localization for Indoor NLoS Environments

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IEEE COMMUNICATIONS LETTERS 2022年第9期26卷 2052-2056页

作者： Sun, Meng Wang, Yunjia Huang, Lu Xu, Shenglei Cao, Hongji Joseph, Wout Plets, David China Univ Min & Technol Sch Environm & Spatial Informat Key Lab Land Environm & Disaster Monitoring MNR Xuzhou 221116 Jiangsu Peoples R China State Key Lab Satellite Nav Syst & Equipment Tech Shijiazhuang 050081 Hebei Peoples R China Univ Ghent Dept Informat Technol WAVES Grp IMEC B-9052 Ghent Belgium

Smartphone-based WiFi ranging using fine time measurement (FTM) is severely impacted by Non-line-of-sight (NLoS) environments, which causes significant positioning errors. To address this problem, we propose a novel WiFi FTM positioning (WFP) approach based on the geomagnetism and enhanced genetic algorithm (EGA), which can simultaneously execute WiFi localization and ranging compensation. Based on the distribution of the ranging error in NLoS environments, a semiparametric error model-based ranging compensation method is proposed. To construct the EGA searching model, geomagnetism-based positioning is adopted and fed to the EGA together with the measured WiFi ranging data and the ranging compensation method. During online localization, the EGA model dynamically compensates for the erroneous ranging data until it finds the optimal position. Experimental results show that the ranging and localization accuracy of this EGA-based WFP are 1.33 m and 1.64 m, being an improvement of 30.7% and 56.5% compared to the uncompensated ranging data and the trilateration algorithm using the weighted least square (WLS) method, respectively.

关键词： Distance measurement Wireless fidelity Measurement uncertainty Nonlinear optics Location awareness Position measurement Data models Indoor localization WiFi FTM ranging compensation geomagnetism NLoS enhanced genetic algorithm

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Efficiency Improvements in Meta-Heuristic algorithms to Solve the Optimal Power Flow Problem

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2016年第6期17卷 631-647页

作者： Reddy, S. Surender Bijwe, P. R. Woosong Univ Dept Railrd & Elect Engn Daejeon South Korea Indian Inst Technol Delhi Dept Elect Engn New Delhi India

This paper proposes the efficient approaches for solving the Optimal Power Flow (OPF) problem using the meta-heuristic algorithms. Mathematically, OPF is formulated as non-linear equality and inequality constrained optimization problem. The main drawback of meta-heuristic algorithm based OPF is the excessive execution time required due to the large number of power flows needed in the solution process. The proposed efficient approaches uses the lower and upper bounds of objective function values. By using this approach, the number of power flows to be performed are reduced substantially, resulting in the solution speed up. The efficiently generated objective function bounds can result in the faster solutions of meta-heuristic algorithms. The original advantages of meta-heuristic algorithms, such as ability to handle complex non-linearities, discontinuities in the objective function, discrete variables handling, and multi-objective optimization, etc., are still available in the proposed efficient approaches. The proposed OPF formulation includes the active and reactive power generation limits, Valve Point Loading (VPL) and Prohibited Operating Zones (POZs) effects of generating units. The effectiveness of proposed approach is examined on IEEE 30, 118 and 300 bus test systems, and the simulation results confirm the efficiency and superiority of the proposed approaches over the other meta-heuristic algorithms. The proposed efficient approach is generic enough to use with any type of metaheuristic algorithm based OPF.

关键词： differential evolution enhanced genetic algorithm meta-heuristic algorithms power system optimization optimal power flow

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Two-layer Reactive Power Optimal Dispatching Model for High-Voltage Distribution Network and Application

Two-layer Reactive Power Optimal Dispatching Model for High-...

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IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Zhang, Jianmin Liu, Neng Lu, Xianchuan Wu, Mengkai Hangzhou Dianzi Univ Sch Automat Hangzhou 310012 Zhejiang Peoples R China State Grid Xinchang Elect Power Supply Co Dispatch Ctr Xinchang 312000 Peoples R China North China Elect Power Univ Sch Elect Engn Beijing 310012 Peoples R China

ISBN: (纸本)9781467381321

Based on characteristics investigation of reactive power dispatch conditions of present high-voltage distribution networks (HVDNs) in China, a two layer optimal reactive power dispatch (ORPD) model has been proposed in which all the decision variables are discrete types in order to reduce the combination decision space;open-source MATPOWER procedure is taken as the power flow calculation module after turning the three windings transformer into two windings model. An enhanced genetic algorithm (EGA) is developed and the simulations have proven that the proposed model and algorithm arc effective and efficient.

关键词： High-voltage distribution network optimal reactive power dispatch MATPOWER enhanced genetic algorithm

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Data Driven Modeling for UGI Gasification Process via a Variable Structure genetic BP Neural Network

Data Driven Modeling for UGI Gasification Process via a Vari...

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International Joint Conference on Neural Networks (IJCNN)

作者： Liu, Shida Hou, Zhongsheng Yin, Chenkun Beijing Jiaotong Univ Sch Elect & Informat Engn Adv Control Syst Lab Beijing Peoples R China

ISBN: (纸本)9781479914845

An enhanced genetic BP neural network with link switches (EGA-VRBPNN) is proposed in this work to address the data-driven modeling problem for the gasification process inside a UGI gasifier. During gasification processes, the online measured gas temperature is crucial but difficult to model its' dynamics via first principles because of the tremendous complexity of the gasification process, which is mainly reflected from severe changes of the gas temperature versus infrequent and small manipulations of parts of the input variables. EGA-VRBPNN, which incorporates a neural networks with link switches (NN-LS) with an enhanced genetic algorithm (EGA) and the Levenberg-Marquardt (LM) algorithm, can not only learn the relationships between control inputs and system outputs from historical data with the help of optimized network structure through combination of the EGA and NN-LS, but also overcome the drawbacks of gradient-based method and make full use of the network's gradient information to achieve a satisfactory accuracy. A set of data collected from the practical fields are applied to modeling via the EGA-VRBPNN, by which the effectiveness of the EGA-VRBPNN is verified.

关键词： Data-driven modeling UGI gasifier neural networks with link switches enhanced genetic algorithm

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Fault-tolerance to Cascaded Link Failures of Video Traffic on Attacked Wireless Networks

Fault-tolerance to Cascaded Link Failures of Video Traffic o...

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IST-Africa Conference (IST-Africa)

作者： Owoade, Ayoade Akeem Osunmakinde, Isaac Olusegun Univ South Africa Sch Comp Coll Sci Engn & Technol ZA-0001 Pretoria South Africa Norfolk State Univ Coll Sci Engn & Technol Comp Sci Dept Norfolk VA 23504 USA

ISBN: (纸本)9781905824670

Research has been conducted on wireless network single link failures. However, cascaded link failures due to fraudulent attacks have not received enough attention, whereas this requires solutions. This research developed an enhanced genetic algorithm (EGA) focused on capacity efficiency and fast restoration to rapidly resolve link-link failures. On complex nodes network, this fault-tolerant model was tested for such failures. Optimal alternative routes and the bandwidth required for quick rerouting of video traffic were generated by the proposed model. Increasing cascaded link failures increases bandwidth usage and causes transmission delay, which slows down video traffic routing. The proposed model outperformed popular Dijkstra models, in terms of time complexity. The survived solution paths demonstrate that the proposed model works well in maintaining connectivity despite cascaded link failures and would therefore be extremely useful in pandemic periods on emergency matters. The proposed technology is feasible for current business applications that require high-speed broadband networks.

关键词： Wireless Network Fraudulent Attacks Link failure Routing Video Fault-tolerance enhanced genetic algorithm Traffic

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Lifetime Maximization for UAV-Enabled Integrated Localization and Communication Networks in Emergency Scenarios 14

Lifetime Maximization for UAV-Enabled Integrated Localizatio...

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14th IEEE International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Yu, Xinyu Liu, Zhenyu Xu, Lianming Wang, Li Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Peoples R China BUPT Natl Pilot Software Engn Sch Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781665450850

The flexible and controllable mobility property makes Unmanned Aerial Vehicles (UAVs) valuable for constructing temporary networks in a harsh environment. However, existing works always focus on enhancing the coverage under the assumption of given positions of users, which degrades the coverage performance in environments without prior locations. In this paper, we propose a divide-conquer UAV deployment strategy for integrated localization and communication networks. Specifically, we first model the UAV deployment problem with the objective of maximizing the regional localization accuracy to locate the users. Then, based on the positions obtained, the lifetime maximization problem is modeled under the constraints of communication and localization requirements. An enhanced genetic algorithm (EGA) is proposed to maximize the UAVs' residual lifetime while meeting the service requirements. Simulation results demonstrate that the proposed deployment strategy can provide more stable communication coverage and obtain longer residual lifetime for the UAVs compared with the existing strategies.

关键词： emergency communication the deployment of UAVs localization enhanced genetic algorithm residual lifetime

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Collaborative Scheduling of Production and Transportation in the Shop-Floor Based on Digital Twin 17

Collaborative Scheduling of Production and Transportation in...

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17th Conference on Industrial Electronics and Applications (ICIEA)

作者： Xiong, Xiaozheng Xu, Wenjun Liu, Jiayi Hu, Yang Wuhan Univ Technol Sch Informat Engn Hubei Key Lab Broadband Wireless Commun & Sensor Wuhan Peoples R China China Ship Dev & Design Ctr Wuhan Peoples R China

ISBN: (纸本)9781665409841

Production and transportation are core parts of a manufacturing enterprise. However, traditional optimization of production and transportation schedule is performed separately, which reduces operational efficiency of the actual system. In this context, a collaborative scheduling mechanism of production and transportation based on digital twin (DT) is proposed. Under the digital twin framework, a collaborative three-stage scheduling model for production, distribution, and purchases was developed with the goal of reducing the time required for transportation in a flexible job shop manufacturing setting. The model is solved using an improved genetic algorithm (EGA) that the chromosomal encoding and decoding are handled in accordance with the specifics of the issue. The digital twin-based model can monitor the physical environment in real time and deal with dynamic interference like urgent insertion of orders in time. Through the case study and experiment analysis, the results demonstrate the effectiveness of the collaborative scheduling strategy and the superiority of the dynamic collaborative scheduling within the framework of digital twin.

关键词： production transportation digital twin enhanced genetic algorithm collaborative scheduling dynamic scheduling

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Comparison of genetic algorithms and particle swarm optimization for optimal power flow including FACTS devices

Comparison of genetic algorithms and particle swarm optimiza...

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IEEE Lausanne Powertech

作者： Kumari, M. Sailaja Priyanka, G. Sydulu, M. Natl Inst Technol Dept Elect Engn Warangal Andhra Pradesh India Natl Inst Technol Warangal Andhra Pradesh India

ISBN: (纸本)9781424421893

This paper describes the performance of two population based search algorithms (genetic algorithms and Particle Swarm Optimization) when applied to Optimal Power Flow (OPF) including Static VAR Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) devices. The OPF optimizes a power system operating objective function, while satisfying a set of system operating constraints. The basic OPF solution is obtained with fuel cost minimization as the objective function and the optimal settings of the power system are determined. OPF can also be formulated for reactive power optimization, as minimization of system active power losses and improving the voltage stability in the system. In the present paper different objective functions that reflect Fuel cost minimization, System power loss minimization, Voltage Stability Enhancement (L-index minimization), Power loss minimization with SVC device and Power loss minimization with combined application of SVC and TCSC devices have been considered. To monitor and improve voltage stability in power system, minimization of sum of squared L-indices of all the load buses is considered as objective function in OPF. This index also guides the optimal location for VAR compensation. During normal operating conditions a planning engineer requires that all line flows and voltages are within limits while minimizing investment (including losses). While during outage conditions, line loading and voltages are again desired within limits while minimizing investment. It is important to obtain feasible solutions with in a minimal amount of engineering time. The numerical results for IEEE 30bas system are presented and a comparison is made between the results obtained using the two evolutionary computation techniques.

关键词： genetic algorithms Particle Swarm Optimization enhanced genetic algorithm Optimal Power Flow Static VAR Compensator thyristor controlled series compensator

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Hub-and-spoke network design for container shipping in inland waterways

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 223卷

作者： Zhou, Saiqi Ji, Bin Song, Yalong Yu, Samson S. Zhang, Dezhi Van Woensel, Tom Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China Deakin Univ Sch Engn Waurn Ponds Vic 3216 Australia Eindhoven Univ Technol Dept Ind Engn & Innovat Sci Eindhoven Netherlands

The enormous growth in the volume of river transportation and the upgrade of inland waterways make inland shipping more and more important in transportation and logistics. This work proposes a hub-and-spoke network design (HSND) for container shipping in inland waterways based on the tree-like structure river. Firstly, the characteristics of the hub-and-spoke network in the inland waterways are presented in detail. Then, an integer linear programming model is proposed to simultaneously determine the optimal hub location, feeder port allocation, and fleet deployment to minimize the total cost of ships, transportation, and transshipment. A decomposition-based math-heuristic method and an enhanced genetic algorithm are then proposed to solve the HSND. A case study based on the traffic on the Yangtze River and extensive computational experiments are conducted to verify the effectiveness of the proposed models and methods. In addition, the impacts of the number of hub ports and the economies of scale resulting from the hub-and-spoke network are investigated from the economic and network structure perspectives, which leads to some managerial insights.

关键词： Hub-and-spoke network design Inland waterway container shipping Integer linear programming model Decomposition-based math-heuristic enhanced genetic algorithm

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